Matching Items (3)

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Algorithmic Prediction of Binding Sites of TNFα/TNFR2 and PD-1/PD-L1

Description

Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be

Predicting the binding sites of proteins has historically relied on the determination of protein structural data. However, the ability to utilize binding data obtained from a simple assay and computationally make the same predictions using only sequence information would be more efficient, both in time and resources. The purpose of this study was to evaluate the effectiveness of an algorithm developed to predict regions of high-binding on proteins as it applies to determining the regions of interaction between binding partners. This approach was applied to tumor necrosis factor alpha (TNFα), its receptor TNFR2, programmed cell death protein-1 (PD-1), and one of its ligand PD-L1. The algorithms applied accurately predicted the binding region between TNFα and TNFR2 in which the interacting residues are sequential on TNFα, however failed to predict discontinuous regions of binding as accurately. The interface of PD-1 and PD-L1 contained continuous residues interacting with each other, however this region was predicted to bind weaker than the regions on the external portions of the molecules. Limitations of this approach include use of a linear search window (resulting in inability to predict discontinuous binding residues), and the use of proteins with unnaturally exposed regions, in the case of PD-1 and PD-L1 (resulting in observed interactions which would not occur normally). However, this method was overall very effective in utilizing the available information to make accurate predictions. The use of the microarray to obtain binding information and a computer algorithm to analyze is a versatile tool capable of being adapted to refine accuracy.

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Created

Date Created
2018-05

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Investing & Information Measurement Theory: Calculated Risk

Description

Dr. Dean Kashiwagi created a new thinking paradigm, Information Measurement Theory (IMT), which utilizes the understanding of natural laws to help individuals minimize decision-making and risk, which leads to reduced stress. In this new paradigm, any given situation can only

Dr. Dean Kashiwagi created a new thinking paradigm, Information Measurement Theory (IMT), which utilizes the understanding of natural laws to help individuals minimize decision-making and risk, which leads to reduced stress. In this new paradigm, any given situation can only have one unique outcome. The more information an individual has for the given situation, the better they can predict the outcome. Using IMT can help correctly "predict the future" of any situation if given enough of the correct information. A prime example of using IMT would be: to correctly predict what a young woman will be like when she's older, simply look at the young woman's mother. In essence, if you can't fall in love with the mother, don't marry the young woman. The researchers are utilizing the concept of IMT and extrapolating it to the financial investing world. They researched different financial investing strategies and were able to come to the conclusion that a strategy utilizing IMT would yield the highest results for investors while minimizing stress. Investors using deductive logic to invest received, on average, 1300% more returns than investors who did not over a 25-year period. Where other investors made many decisions and were constantly stressed with the tribulations of the market, the investors utilizing IMT made one decision and made much more than other investors. The research confirms the stock market will continue to increase over time by looking at the history of the stock market from a birds-eye view. Throughout the existence of the stock market, there have been highs and lows, but at the end of the day, the market continues to break through new ceilings. Investing in the stock market can be a dark and scary place for the blind investor. Using the concept of IMT can eliminate that blindfold to reduce stress on investors while earning the highest financial return potential. Using the basis of IMT, the researchers predict the market will continue to increase in the future; in conclusion, the best investment strategy is to invest in blue chip stocks that have a history of past success, in order to capture secure growth with minimal risk and stress.

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Created

Date Created
2015-05

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Player Optimization in the National Football League: Creating a Winning Franchise

Description

The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project

The NFL is one of largest and most influential industries in the world. In America there are few companies that have a stronger hold on the American culture and create such a phenomena from year to year. In this project aimed to develop a strategy that helps an NFL team be as successful as possible by defining which positions are most important to a team's success. Data from fifteen years of NFL games was collected and information on every player in the league was analyzed. First there needed to be a benchmark which describes a team as being average and then every player in the NFL must be compared to that average. Based on properties of linear regression using ordinary least squares this project aims to define such a model that shows each position's importance. Finally, once such a model had been established then the focus turned to the NFL draft in which the goal was to find a strategy of where each position needs to be drafted so that it is most likely to give the best payoff based on the results of the regression in part one.

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Created

Date Created
2015-05